Tag: ai

Azure Machine Learning Service allows you to easily deploy compute for training and inference via a machine learning workspace. Although one of the compute types is Kubernetes, the workspace is a bit picky about the node VM sizes. I wanted to use two Standard_NC6s_v3 instances with NVIDIA Tesla V100 GPUs but that was not allowed. Other GPU instances, such as the Standard_NC6 type (K80 GPU) can be deployed from the workspace.

Luckily, you can deploy clusters on your own and then attach the cluster to your Azure Machine Learning workspace. You can create the cluster with the below command. Make sure you ask for a quota increase that allows 12 cores of Standard_NC6s_v3.

Before I ran the above command, I created an Azure Machine Learning workspace to a resource group called ml-rg. The above command was run with RESOURCE_GROUP set to ml-rg and NAME set to mlkub. After a few minutes, you should have your cluster up and running. Be mindful of the price of this cluster. GPU instances are not cheap!

Now we can Add Compute to the workspace. In your workspace, navigate to Compute and use the + Add Compute button. Complete the form as below. The compute name does not need to match the cluster name.

After a while, the Kubernetes cluster should be attached:

Manually deployed cluster attached

Note that detaching a cluster does not remove it. Be sure to remove the cluster manually!

You can now deploy container images to the cluster that take advantage of the GPU of each node. When you a deploy an image marked as a GPU image, Azure Machine Learning takes care of all the parameters that allow your container to use the GPU on the Kubernetes node.

The screenshot below shows a deployment of an image that can be used for inference. It uses an ONNX ResNet50v2 model.

Deployment of container for scoring (inference; ResNet50v2)

With the below picture of a cat, the model used by the container guesses it is an Egyptian Cat (it’s not but it is close) with close to 94% certainty.

Egyptian Cat (not)

Using your own compute with the Azure Machine Learning service is very easy to do. The more interesting and somewhat more complicated parts such as the creation of the inference container that supports GPUs is something I will discuss in a later post. In a follow-up post, I will also discuss how you send image data to the scoring container.

To use one of the APIs you need to provision it in an Azure subscription. After provisioning, you will get an endpoint and API key. Every time you want to classify an image or detect sentiment in a piece of text, you will need to post an appropriate payload to the cloud endpoint and pass along the API key as well.

What if you want to use these services but you do not want to pass your payload to a cloud endpoint for compliance or latency reasons? In that case, the Cognitive Services containers can be used. In this post, we will take a look at the Text Analytics containers, specifically the one for Sentiment Analysis. Instead of deploying the container manually, we will deploy the container with IoT Edge.

IoT Edge Configuration

To get started, create an IoT Hub. The free tier will do just fine. When the IoT Hub is created, create an IoT Edge device. Next, configure your actual edge device to connect to IoT Hub with the connection string of the device you created in IoT Hub. Microsoft have a great tutorial to do all of the above, using a virtual machine in Azure as the edge device. The tutorial I linked to is the one for an edge device running Linux. When finished, the device should report its status to IoT Hub:

Once you have your edge device up and running, you can use the following command to obtain the status of your edge device: sudo systemctl status iotedge. The result:

Deploy Sentiment Analysis container

With the IoT Edge daemon up and running, we can deploy the Sentiment Analysis container. In IoT Hub, select your IoT Edge device and select Set modules:

In Set Modules you have the ability to configure the modules for this specific device. Modules are always deployed as containers and they do not have to be specifically designed or developed for use with IoT Edge. In the three step wizard, add the Sentiment Analysis container in the first step. Click Add and then select IoT Edge Module. Provide the following settings:

Although the container can freely be pulled from the Image URI, the container needs to be configured with billing info and an API key. In the Billing environment variable, specify the endpoint URL for the API you configured in the cloud. In ApiKey set your API key. Note that the container always needs to be connected to the cloud to verify that you are allowed to use the service. Remember that although your payload is not sent to the cloud, your container usage is. The full container create options are listed below:

In HostConfig we ask the container runtime (Docker) to map port 5000 of the container to port 5000 of the host. You can specify other create options as well.

On the next page, you can configure routing between IoT Edge modules. Because we do not use actual IoT Edge modules, leave the configuration as shown below:

Now move to the last page in the Set Modules wizard to review the configuration and click Submit.

Give the deployment some time to finish. After a while, check your edge device with the following command: sudo iotedge list. Your TextAnalytics container should be listed. Alternatively, use sudo docker ps to list the Docker containers on your edge device.

Testing the Sentiment Analysis container

If everything went well, you should be able to go to http://localhost:5000/swagger to see the available endpoints. Open Sentiment Analysis to try out a sample:

Summary

IoT Edge is a great way to deploy containers to edge devices running Linux or Windows. Besides deploying actual IoT Edge modules, you can deploy any container you want. In this post, we deployed a Cognitive Services container that does Sentiment Analysis at the edge.